Pattern recognition classifies observed or measured data into certain categories using statistical or syntactic (neural network) approaches. Applications include industrial inspection, biometrics such as fingerprint, iris, and face recognition, weather map analysis for forecasting, character recognition, speech recognition, and bar code recognition. Many of these use the spatial form of data such as pictures or video images.
Fingerprint recognition is widely used for personal identification or forensic investigation. Fingerprint is scanned by optical, silicon, or ultrasound technologies. The optical method uses the CCD (Charge Coupled Device) to convert a fingerprint image into digital form. The accuracy of optical scanning can be degraded by latent images left from previous users and wear of coating and CCD. The silicon technology provides a high resolution image using the DC capacitance between a silicon sensor and the finger, but careful scanning process and verification is required due to its inherent small sensor size, which makes capturing the center of a finger difficult. The ultrasound technology is not widely used yet but considered as a most accurate and robust fingerprint scanning method by providing a large platen size and penetrating the dirt and residue on the platen and the finger.
An iris scan is known as a most accurate biometric authentication method. The iris is the visible colored area surrounding pupil, which controls the amount of the light entering the eye. Each individual iris has hundreds of comparison features and very stable over time. The iris is scanned by a camera system with an eye safe illumination device from several inches to several feet away.
While these biometric applications provide great accuracy, these systems require user's cooperation because the finger or the iris need to be carefully positioned relative to the sensor, which can cause problems in a high-throughput application or surveillance system.
Face recognition for personal authentication or surveillance is less intrusive; in other words, need less or no subject's cooperation. A face recognition system extracts a face image from a surveillance video system, and aligns and normalizes the face image to compare with images in the facial database. There exists a plurality of fiducial points in the human face which can be used in face recognition including distance between eyes, width of nose, depth of eye sockets, jaw line, and the like. Popular face recognition algorithms include eigenface method, hidden Markov model approach, and dynamic link matching method. Majority of face recognition algorithms uses two-dimensional images and their accuracy is greatly subject to environmental factors including face pose, illumination, cosmetics, and face expression.
Many literatures including C. Hesher, A. Srivastava, and G. Erlebacher, “A Novel Technique for Face Recognition using Range Images,” the Seventh International Symposium on Signal Processing and its Applications, 2003, and G. Medioni and R. Waupotitsch, “Face Modeling and Recognition in 3-D,” Proceedings of the IEEE International Workshop on Analysis and Modeling of Faces and Gestures, pages 232–233, October 2003, claim that face recognition accuracy and reliability can be remarkably increased by employing three-dimensional facial geometries because three-dimensional images represent the internal anatomical structure which is less varied by environmental factors than the external appearance.
The recent survey of three-dimensional face recognition algorithms is given by Kevin W. Bowyer, Kyong Chang, and Patrick Flynn, “A Survey Of 3D and Multi-Modal 3D+2D Face Recognition,” Notre Dame Department of Computer Science and Engineering Technical Report, TR-2004-22-9, 2004.
While three-dimensional face recognition approaches promise to resolve their two-dimensional counterpart problems, a major challenge on three-dimensional face recognition systems is the acquisition of three-dimensional images for both facial database and surveillance. Conventionally, three-dimensional images can be obtained from a stereoscopic imaging system or a structured-light system. Bowyer et al. described the problems related to these conventional sensors, which include a narrow depth of field, lower depth resolution, and longer acquisition time.
The aforementioned problems can be more critical to the non-intrusive approach, where a subject is unaware of surveillance or moving. In this case, the subject can not be positioned at the optimal working range of an imaging system, while most three-dimensional imaging systems have a limited range of depth of field. Further, the research suggested that effective depth resolution for successful three-dimensional face recognition is less than 1 mm (K. Chang, K. Bowyer, P. Flynn, and X. Chen, “Multimodal Biometrics Using Appearance, Shape and Temperature,” The 6th IEEE International Conference on Automatic Face and Gesture Recognition, May 2004), which requires 200 or more pixels in the depthwise direction of a three-dimensional image considering the size of the human head. Conventional three-dimensional imaging systems require a half to ten seconds to obtain this resolution image. In this speed, it is almost impossible to obtain usable images of the subject who is moving.
Beside face recognition, many other pattern recognition problems such as industrial inspection and medical image registration also can have benefits from the three-dimensional approach. However, three-dimensional imaging systems need to be improved to satisfy current demands for three-dimensional pattern recognition.